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Single-cell atlas of transcriptomic vulnerability across multiple neurodegenerative and neuropsychiatric diseases

Lee, Donghoon, Koutrouli, Mikaela, Masse, Nicolas Y., Hoffman, Gabriel E., Kinrot, Seon, Wang, Xinyi, N.M., Prashant, Pjanic, Milos +24 more·2024
DOI 10.1101/2024.10.31.24316513PMC PMC12833908
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Abstract

Neurodegenerative and neuropsychiatric diseases impose a significant societal and public health burden. However, our understanding of the molecular mechanisms underlying these highly complex conditions remains limited. To gain deeper insights into the etiology of different brain diseases, we used specimens from 1,494 unique donors to generate a population-scale single-cell transcriptomic atlas of the human dorsolateral prefrontal cortex (DLPFC), comprising over 6.3 million individual nuclei. The cohort includes neurotypical controls as well as donors affected by eight common and complex brain disorders: Alzheimer's disease (AD), diffuse Lewy body disease (DLBD), vascular dementia (Vas), Parkinson's disease (PD), tauopathy, frontotemporal dementia, schizophrenia, and bipolar disorder. We show that inter-individual variation accounts for a substantial portion of gene expression variation in the DLPFC. By comparing transcriptomic variation across diseases, we reveal universal signatures enriched in basic cellular functions such as mRNA splicing and protein localization. After discounting these cross-disease signatures, we show strong genetic and transcriptomic concordance among AD, DLBD, Vas, and PD, largely driven by alteration of synaptic signaling functions in neurons. Furthermore, we characterize transcriptomic variation among different AD phenotypes that were distinct from healthy aging. We uncover mitigating effects of interneurons and aggravating effects of immune and vascular cells in AD dementia. Further exploring the effect of the neuropsychiatric symptoms frequently accompanying AD, we identify a link to deep layer excitatory neurons. By constructing transcriptome trajectories that capture AD progression, we show cell-type specific responses implicated in early and late stages of AD. Our atlas provides an unprecedented perspective of the transcriptomic landscape in neurodegenerative and neuropsychiatric diseases, shedding light on shared and distinct processes involving the neuro-immune-vascular systems, and identifying potential targets for therapeutic intervention.

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  • hsa-miR-181a-5p has been implicated in oligodendrocyte maturation and suggested as a potential epigenetic biomarker of multiple sclerosis.

    “previously implicated in oligodendrocyte maturation 56 and suggested as a potential epigenetic biomarker of multiple sclerosis 57”

    From Human brain microRNAs exhibit cell-type specificity and age dependence (2026) · cites this paper as [57]

    The Inter-individual variation section discusses genes associated with multiple sclerosis, and the Nonlinear dynamics section discusses a disease-associated gene module, but neither addresses hsa-miR-181a-5p. Searches for the miRNA, oligodendrocyte maturation, and an epigenetic biomarker of multiple sclerosis found no passage supporting the attributed claim. Page numbers were not available in our copy. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • hsa-miR-181a-5p was identified among oligodendrocyte-enriched miRs.

    “We also identified hsa-miR-181a-5p, previously implicated in oligodendrocyte maturation 56 and suggested as a potential epigenetic biomarker of multiple sclerosis 57 .”

    From Human brain microRNAs exhibit cell-type specificity and age dependence (2026) · cites this paper as [57]

  • The miR-33 family is associated with lipid metabolism in the brain, and the miR-181b family was implicated in myelin-assisted axon regeneration.

    “The miR-33 family is associated with lipid metabolism in the brain 58 , and the miR-181b family was implicated in myelin-assisted axon regeneration 59 .”

    From Human brain microRNAs exhibit cell-type specificity and age dependence (2026) · cites this paper as [58]

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Authors
Lee, D., Koutrouli, M., Masse, N. Y., Hoffman, G. E., Kinrot, S., Wang, X., N.M., P., Pjanic, M., Clarence, T., Tsetsos, F., Mathur, D., Burstein, D., Therrien, K., Hong, A., Casey, C., Shao, Z., Alvia, M., Argyriou, S., Monteiro Fortes, J., Katsel, P., Auluck, P. K., Barnes, L. L., Marenco, S., Bennett, D. A., PsychAD Consortium, Jensen, L. J., Girdhar, K., Voloudakis, G., Haroutunian, V., Bendl, J., Fullard, J. F., & Roussos, P.
Publisher
openRxiv
Published
2024-11-04
DOI
10.1101/2024.10.31.24316513
PMC
PMC12833908
Cited by
20
Type
Preprint
Licence
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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